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Spatio-temporal predictive modelling of Rift Valley Fever in response to climate change in Garissa, Kenya Rift Valley Fever (RVF) is a mosquito-borne viral disease that has a significant public threat to humans and livestock in Kenya. Major cyclic epidemics have occurred in 1997-1998 and 2006-2007 in Garissa, Kenya closely associated with El Nino Phenomenon. This study proposes to assess adaptation and mitigation strategies in pastoral livestock production systems and investigate the application of spatio-temporal model-based approaches in future prediction and burden analysis of Rift Valley Fever in relation to climate change stress factors. The research evaluates the current situation of RVF in Garissa by describing epidemiological risk factors, spatiotemporal distribution, impact and economic burden, predictive warning systems and control strategies. Using participatory epidemiology information on indigenous knowledge, attitude and practices in RVF management in response to climate change will be analysed. Disease burden estimates (morbidity, mortality, interventional costs) related RVF outbreaks will be used in estimation of economic losses due to changing climatic conditions. Predictive models based on existing incidence data and climatic parameters will be explored for increased preparedness to disease outbreaks. The study aims to provide empirical evidence on the vulnerability of livestock systems to improve livelihood resilience by quantification of the temporal and spatial patterns of climate risk for spread of RVF. The findings would improve our understanding of the geographical distribution of RVF and encourage wider application of these methods in study of other animal diseases. The research output will aid vulnerable communities to make choices and take actions that lead to sustainable livelihoods in the face of climate change. Prof. S. G. Kiama, BVM, MSC, PHD Dr. Gerald Muchemi Dr. Bett Dr. Thumbi Mwangi Dr. Mark Nanyingi (Phd STUDENT) Supported by Colorado State University